MIT 9 29 - Spike Sorting (12 pages)

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Spike Sorting



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Spike Sorting

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Lecture Notes


Pages:
12
School:
Massachusetts Institute of Technology
Course:
9 29 -

Unformatted text preview:

Spike Sorting Goal Extract neural spike trains from MEA electrode data Method 1 Convolution of template spikes Method 2 Sort by spikes features Cluster Cutting Advantages Better separation Requires less information Disadvantages Computationally intensive Remap2pin02 Spikes Selected Features 1 Max peak height 2 Voltage difference between max and second max 3 Sum of max positive and max negative peaks 4 Time between max positive and max negative peaks 5 Max width of a polarization Features 1 Max peak height Color 2 Voltage difference between max and second max Z axis 3 Sum of max positive and max negative peaks Y axis 4 Time between max positive and max negative peaks X axis 5 Max width of a polarization Size Features Plot Remap2pin02 Spikes Training Features Plot Training Features Plot Training Features Plot Future Direction Optimal feature choice Training algorithm Bayesian clustering Nearest neighbor Support Vector Machine Neural Network Conclusion Data suggests we should be able to isolate individual neural firing patterns from MEA data Use MEA data to model and study network of neurons in culture



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